Correlating mobile client density and power variation for adaptive path loss correction

ABSTRACT

Presented herein are techniques for using mobile client density to compensate for variations in path loss between neighboring access points. In one example, a device (e.g., wireless controller) determines one or more mobile client density variation trends in a wireless network location and determines one or more neighbor message power variation trends between at least first and second access points within the wireless network location. The device generates one or more correlation bias factors using the mobile client density variation trends and the neighbor message power variation trends. The device determines a path loss between at least the first and second access points based on the correlation bias factor and data associated with neighbor messages sent between the first and second access points.

TECHNICAL FIELD

The present disclosure relates to adaptive path loss correction inwireless networks.

BACKGROUND

Wireless (e.g., Wi-Fi™) networks are local area communication networksthat enable electronic devices to wirelessly exchange data or towirelessly connect to network resources, such as the Internet. Wirelessnetworks are becoming increasingly popular and are installed in a largenumber of homes, offices, public locations, etc. In a typicaldeployment, the wireless network is created/provided by multiple accesspoints. In general, access points are devices that include a radiotransmitter/receiver (radio) that is used to bridge the wireless andwired (e.g., Ethernet) network communication media.

In certain circumstances, wireless connectivity may be available to alarge number of wireless electronic devices having wirelesscapabilities, referred to herein as “wireless client devices” or“wireless clients,” within a certain area. Such environments mayinclude, for example, stadiums, movie theaters, malls, conventioncenters, offices, or other locations. In such arrangements, the wirelessnetwork may include numerous access points deployed in a relativelysmall area (in comparison to traditional home or enterprise wirelessdeployments) in order to support a high density of clients.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are schematic diagrams illustration a portion of awireless network configured to implement adaptive path loss correctiontechniques, in accordance with certain embodiments presented herein.

FIG. 2 is a schematic diagram illustration a portion of another wirelessnetwork configured to implement adaptive path loss correctiontechniques, in accordance with certain embodiments presented herein.

FIG. 3 is a flowchart of a method, in accordance with certainembodiments presented herein.

FIG. 4 is a graph illustrating example wireless client device densityvariation trends during a time period, in accordance with certainembodiments presented herein.

FIG. 5 is a graph illustrating neighbor message power variation trendsfor an access point during a period time and at a first frequency, inaccordance with certain embodiments presented herein.

FIG. 6 is a graph illustrating neighbor message power variation trendsfor an access point during a period time and at a second frequency, inaccordance with certain embodiments presented herein.

FIG. 7 is a graph illustrating a mobile client density trend with twoneighbor message power variation trends during a period time, inaccordance with certain embodiments presented herein.

FIG. 8 is a flow diagram illustrating an example adaptive path losscorrection technique, in accordance with certain embodiments presentedherein.

FIGS. 9A, 9B, and 9C are diagrams illustrating varying correlation biasfactors for adaptive path loss correction, in accordance with certainembodiments presented herein.

FIG. 10 is a block diagram of a wireless controller, in accordance withcertain embodiments presented herein.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

Presented herein are techniques for using mobile client density tocompensate for variations in path loss between neighboring accesspoints. In one example, a device (e.g., wireless controller) determinesone or more mobile client density variation trends in a wireless networklocation and determines one or more neighbor message power variationtrends between at least first and second access points within thewireless network location. The device generates one or more correlationbias factors using the mobile client density variation trends and theneighbor message power variation trends. The device determines a pathloss between at least the first and second access points based on thecorrelation bias factor and data associated with neighbor messages sentbetween the first and second access points.

EXAMPLE EMBODIMENTS

Wireless networks are created/provided by multiple access points (APs)that each include a radio transmitter/receiver (radio) that is used tobridge the wireless and wired (e.g., Ethernet) network communicationmedia. Radio Resource Management (RRM) is used in wireless networks tocontrol certain operations of the access points, such as dynamic channelassignment (DCA), transmit power control (TPC), Flexible RadioAssignment (FRA), Coverage Hole Detection and Mitigation (CDM), etc.

In traditional arrangements, Radio Resource Management relies onover-the-air messages exchanged between access points to determine thepath loss between (e.g., relative Radio Frequency (RF) proximity of)neighboring access points. The messages exchanged between access pointsare sometimes referred to herein as “neighbor messages” or “accesspoint-to-access point (AP-to-AP) messages.” In certain arrangements, theneighbor messages may be Neighbor Discovery Protocol (NDP) packets,although other types of messages (e.g., 802.11 beacons, off-channel orOver-the-Air (OTA) messages, etc.) may be used in different deployments.

In general, the Radio Resource Management operations, and thus theaccess point operations (e.g., DCA, TPC, etc.), rely on these neighbormessages. More particularly, inter-AP radio frequency (RF) measurements(e.g., inter-AP Received Signal Strength Indication (RSSI) values) areobtained from the neighbor messages and then used to determine the pathloss (RF proximity) between access points. That is, neighboring accesspoint information, such as AP-to-AP RSSI values, etc., are used todetermine path loss between access points in wireless networks. Forexample, in certain deployments, access point radio cell size may beoptimized as part of a TPC process that utilizes inter-AP NDP packets toensure that nearby access points have adequate cell overlap with oneanother, while minimizing co-channel contention. The optimal cell sizeis computed by leveraging RSSI from the nearby access points to computetheir relative path loss or RF distance and the cell size canaccordingly expand or shrink to cater to the needs of wireless clientdevices while minimizing coverage holes.

With modernization and improvements in new enterprise buildings, officespaces, warehouses, stadiums, etc., more and more wireless networks arebeing deployed in sites/environments in which traditionalomni-directional antennas do not meet the coverage requirements. Assuch, these deployments are increasingly using directional antennasand/or include omni-directional neighboring access points that do nothave clear line-of-sight (LOS) with one another. Deployments in whichneighboring access points do not have clear LOS and/or includedirectional antennas are particularly susceptible to variations in pathloss (PL).

Additionally, a majority of the Radio Resource Management operations andRF calibrations are initially optimized within a deployment while theparticular environment/site is empty (i.e., minimal wireless clientdevices and users are present). However, in practice, a given wirelessnetwork site may, at different times, have different numbers of personsphysically present within the wireless network site. The bodies of thesepersons present in a wireless network site will absorb some portion ofthe neighbor messages sent between neighboring access points, therebycausing variations in path loss (e.g., absorption of the RF signals mayresult in weaker received signals, thereby resulting in a determinationthat neighboring access points are farther apart then they actuallyare). This results in a mis-calculation of the path loss (RF proximity)between neighboring access points. Since, as noted, path loss is used aninput to the Radio Resource Management operations, the lack of accuratepath loss determinations also affects the operation of the wirelessnetwork (e.g., improper channel assignment, incorrect transmit powercontrol, etc.). These factors also pose intricate challenges todetermine optimal transmit cell size in deployments in which neighboringaccess points do not have clear LOS and/or include directional antennas

Accordingly, presented herein are techniques for using mobile clientdensity to compensate for variations in path loss between neighboringaccess points. In particular, a device (e.g., wireless controller)determines one or more mobile client density variation trends in awireless network location. The one or more mobile client densityvariation trends are used as measure of the density of the moving bodies(persons) within the wireless network location during a time period. Thedevice also determines one or more neighbor message power variationtrends between at least first and second access points within thewireless network location. The one or more neighbor message powervariation trends represent the variations in the power of neighbormessages sent to and/or from one or more of the first or second accesspoints during the time period.

In the examples presented herein, the device generates one or morecorrelation bias factors (correlation coefficients) from the one or moremobile client density variation trends and the one or more neighbormessage power variation trends. The one or more correlation bias factorsindicate how the mobile client density, and more particularly thephysical presence of persons (bodies), within the wireless networklocation affect the power of neighbor messages sent between the firstand second access points. The one or more correlation bias factors areused as offset/weighting/adjustments factors in the determination of apath loss between at least the first and second access points. That is,a path loss between at least the first and second access points isdetermined based on neighbor messages sent between the first and secondaccess points, and the one or more correlation bias factors whichcorrect for the physical presence of persons (bodies), within thewireless network location.

FIGS. 1A and 1B are schematic diagrams illustrating a simplified portionof a wireless network 110, in which the adaptive path loss correctiontechniques presented herein may be employed. The wireless network 110may include a plurality of access points that provide wirelessconnectivity to various wireless electronic devices (wireless clientdevices or wireless clients) present in the vicinity of a location/site(e.g., office, stadium, etc.). For ease of illustration, only two accesspoints 120(A) and 120(B) are shown in FIGS. 1A and 1B. Access point120(A) is referred to herein as “access point A,” while access point120(B) is sometimes referred to herein as “access point B.” Also merelyfor ease of illustration, the wireless network 110 is described asservicing a stadium. It is to be appreciated that embodiments presentedherein may be used in other wireless networks that provide wirelessconnectivity to other types of sites/environments.

Also shown in FIGS. 1A and 1B is a wireless controller 122. The wirelesscontroller 122 is a centralized device configured to manage the accesspoints in wireless network 110. To this end, wireless controller 112includes a path loss (RF proximity) determination module 124, anadaptive path loss correction module 125, and a Radio ResourceManagement module 126. Further details of the path loss determinationmodule 124, the adaptive path loss correction module 125, and the RadioResource Management module 126 are provided below. The wirelesscontroller 122 is connected to the access points 120(A) and 120(B) via,for example, a local area (or wide area) wired network. For simplicity,the intervening wired network is not shown in FIGS. 1A and 1B.

In the example of FIGS. 1A and 1B, the access points 120(A) and 120(B)each comprise a plurality of directional antennas 127 capable ofproducing antenna beams. Shown in FIGS. 1A and 1B is an antenna beam130(A) generated by one or more of the directional antennas 127 ofaccess point 120(A), and an antenna beam 130(B) generated by one or moreof the directional antennas 127 of access point 120(B).

FIG. 1A illustrates two arrows, namely arrow 132(A) from access point120(A) to access point 120(B), and arrow 132(B) from access point 120(B)to access point 120(A). Arrow 132(A) represents the neighbor messagesreceived at access point 120(B) from access point 120(A) (e.g., “Y”decibels (dB) with reference to one milliwatt (mW) (dBm), at a transmitpower “A” (i.e., TxPower A)). Arrow 132(B) represents the neighbormessages received at access point 120(A) from access point 120(B) (e.g.,“X” dBm, at a transmit power “B” (i.e., TxPower B)). In FIG. 1A, TxPowerA is substantially equal to TxPower B, then X dBm is approximately equalto Y dBm.

FIG. 1B illustrates two arrows, namely arrow 134(A) from access point120(A) to access point 120(B), and arrow 134(B) from access point 120(B)to access point 120(A). Arrow 134(A) represents the neighbor messagesreceived at access point 120(B) from access point 120(A) (e.g., “Y*”dBm, at a transmit power “A” (i.e., TxPower A)). Arrow 132(B) representsthe neighbor messages received at access point 120(A) from access point120(B) (e.g., “X*” dBm, at a transmit power “B” (i.e., TxPower B)).

As shown by arrows 132(A)-132(B) and 134(A)-134(B), the neighbormessages sent between access points 120(A) and 120(B) are reflected toone another via a surface (e.g., floor) 136. However, FIGS. 1A and 1Bgenerally illustrate the wireless network site (e.g., stadium) in twodifferent situations/states. In particular, in FIG. 1A, the stadium issubstantially empty/vacant, meaning there are minimal persons (bodies)on the surface 136. FIG. 1A illustrates the typical circumstances inwhich the Radio Resource Management module 126, and thus operation ofthe access points 120(A) and 120(B), are initially set/programmed.

However, in FIG. 1B, the stadium is at least partially occupied, meaningthere are a number of persons (bodies) on the surface 136. In suchcircumstances, the presence of the bodies on surface 136 will absorbsome portion of the neighbor messages (RF signals) 134(A) and 134(B)that are sent between neighboring access points 120(A) and 120(B),thereby causing variations in computed path loss using conventionaltechniques. That is, absorption of some portion of the neighbor messages134(A) and 134(B) may result in weaker received signals at each of theaccess points 120(A) and 120(B), thereby resulting, in conventionalarrangements, a determination that neighboring access points are fartherapart then they actually are.

More specifically, the wireless controller 122 is configured to receive,from the access points 120(A) and 120(B), information/data associatedwith neighbor messages 132(A)-132(B) and neighbor messages134(A)-134(B). This data may be the inter-AP measurements or data thatmay be used to generate the inter-AP measurements. The path lossdetermination module 124 is configured to use the received data tocalculate a path loss between the access points 120(A) and 120(B). Inaccordance with embodiments presented herein, the adaptive path losscorrection module 125 is configured to generate an adjusted path lossthat accounts for the presence of persons (bodies) at the surface 136(e.g., in FIG. 1B).

In particular, as described further below, the adaptive path losscorrection module 125 is configured to generate one or more correlationbias factors (correlation coefficients) using one or more mobile clientdensity variation trends and one or more neighbor message powervariation trends determined for the wireless network location (e.g., thestadium or part of the stadium). The path loss determination module 124determines the path loss between the access points 120(A) and 120(B)based on the one or more correlation bias factors and information/dataassociated with neighbor messages 132(A)-132(B) and neighbor messages134(A)-134(B). The adjusted path loss may then be used by the RadioResource Management module 126 to set/configure (e.g., adjust)operations (e.g., TCP, FRA, etc.) of the access points 120(A) and/or120(B).

FIGS. 1A and 1B illustrate an example which the adaptive path losscorrection techniques presented herein are implemented in connectionwith access points with directional antennas and no LOS with oneanother. It is to be appreciated that this example is merelyillustrative and that the techniques presented herein may be implementedin connection with other deployments.

For example, FIG. 2 is a schematic illustrating a simplified portion ofa wireless network 210, in which the adaptive path loss correctiontechniques presented herein may be employed. The wireless network 210may include a plurality of access points 220 that provide wirelessconnectivity to various electronic devices (clients) present in thevicinity of a location/site (e.g., office building, school, etc.).Merely for ease of illustration, the wireless network 210 is describedas servicing an office space/section 237 of an office building,sometimes referred to herein as office space 237. It is to beappreciated that embodiments presented herein may be used in otherwireless networks that provide wireless connectivity to other types ofsites/environments.

Also shown in FIG. 2 is a wireless controller 222. The wirelesscontroller 222 is a centralized device configured to manage the accesspoints 220 in wireless network 210. To this end, wireless controller 212includes a path loss (RF proximity) determination module 224, anadaptive path loss correction module 225, and a Radio ResourceManagement module 226. Further details of the path loss determinationmodule 224, the adaptive path loss correction module 225, and the RadioResource Management module 226 are provided below. The wirelesscontroller 222 is connected to the access points 220 via, for example, alocal area (or wide area) wired network. For simplicity, the interveningwired network is not shown in FIG. 2.

In the example of FIG. 2, the access points 220 generally includeomnidirectional antennas. However, due to the physical layout of theoffice building, neighboring access points may have differentrelationships with one another. For example, certain neighboring accesspoints may be “LOS neighbors,” meaning there is a direct LOS therebetween for exchanging neighbor messages. Other neighboring accesspoints may be “Non-LOS neighbors,” meaning there is no direct LOS therebetween for exchanging neighbor messages. In FIG. 2, an LOS neighborrelationship between two access points is shown by solid bi-directionalarrows 231, while a Non-LOS relationship between two access points isshown by dashed bi-directional dashed arrows 233. As shown in FIG. 2, anaccess point may be both an LOS neighbor and a Non-LOS neighbor fordifferent access points.

The office building of FIG. 2 may be used in differentsituations/states. In particular, at certain times, the office buildingis substantially empty/vacant, meaning there are minimal persons(bodies) present within the office space 237. This is the typicalcircumstances in which the Radio Resource Management module 226, andthus operation of the access points 220, are initially set/programmed.In contrast, in other circumstances, office space 237 may be occupied,meaning there are a significant number of persons (bodies) in the officespace 237. In such circumstances, the presence of the bodies in theoffice space 237 will absorb some portion of the neighbor messages (RFsignals) that are sent between neighboring access points 220, therebycausing variations in computed path loss. That is, absorption of someportion of the neighbor messages may result in weaker received signalsat each of the access points 220, thereby resulting, in conventionalarrangements, a determination that neighboring access points are fartherapart then they actually are.

More specifically, the wireless controller 222 is configured to receive,from the access points 220, information/data associated with neighbormessages sent between various pairs of access points. This data may bethe inter-AP measurements or data that may be used to generate theinter-AP measurements. The path loss determination module 224 isconfigured to use the received data to calculate a path loss (RFproximity) between neighboring access points. In accordance withembodiments presented herein, the adaptive path loss correction module225 is configured to generate an adjusted path loss that accounts forthe presence of persons (bodies) in the office space 237.

In particular, as described further below, the adaptive path losscorrection module 225 is configured to generate one or more correlationbias factors (correlation coefficients) using one or more mobile clientdensity variation trends and one or more neighbor message powervariation trends determined for the wireless network location (e.g., theoffice space 237 or part of the office space 237). The path lossdetermination module 224 determines the path loss between neighboringaccess points based on the one or more correlation bias factors andinformation/data associated with neighbor messages. The adjusted pathloss may then be used by the Radio Resource Management module 226 toset/configure (e.g., adjust) operations (e.g., TCP, FRA, etc.) of one ormore of the access points 220.

FIG. 3 is a flowchart of a method 350 in accordance with certainembodiments presented herein. For ease of description, method 350 willbe generally described with reference to wireless network 210 of FIG. 2.

Method 350 begins at 352 where one or more “mobile client densityvariation trends” are determined for a wireless network location. Asdescribed further below, the wireless network location may be, forexample, the physical space associated with an entire wireless networkor a section/segment of the physical space associated with a wirelessnetwork. Further details regarding determination of mobile clientdensity variation trends for a wireless network location are providedbelow with reference to FIG. 4.

At 354, method 350 includes determining one or more “neighbor messagepower variation trends” between at least first and second access pointswithin the wireless network location. Further details regardingdetermination of neighbor message power variation trends between atleast first and second access points are provided below with referenceto FIGS. 5 and 6.

At 356, method 350 further includes generating at least one “correlationbias factor” based on the one or more mobile client density variationtrends and the one or more neighbor message power variation trends.Further details regarding generation of correlation bias factors areprovided below with reference to FIG. 7.

At 358, method 350 also includes determining a path loss between the atleast the first and second access points based on the at least onecorrelation bias factor and data associated with neighbor messages sentbetween the first and second access points. Further details regardingdetermination of a path loss based on the correlation bias factor anddata associated with neighbor messages are provided elsewhere herein.

As noted above, FIGS. 4, 5, 6, and 7 are graphs illustrating furtherdetails of the adaptive path loss correction techniques that mayimplemented, for example, in connection with FIGS. 1A, 1B, and 2. Merelyfor ease of illustration, FIGS. 4, 5, 6, and 7 are generally describedwith reference to wireless network 210 of FIG. 2. However, it is to beappreciated that the techniques presented herein may be implemented inother deployments.

As noted above, correlation bias factors are generated, in part, basedon one or more mobile client density variation trends within a wirelessnetwork location, such as office space 237 or a section/segment ofoffice space 237. As such, in accordance with examples presented herein,the adaptive path loss correction module 225 (or another entity) isconfigured to determine mobile client density variation trends withinthe office space 237.

As used herein, the phrase “mobile client density variation trend”refers to fluctuations or changes in the presence of mobile wirelessclient devices (mobile clients) within an area of a wireless networklocation (e.g., a section of office space 237) over a time period.Referring specifically to FIG. 4, shown is a graph 460 having a first(horizontal) axis 461 and a second (vertical) axis 463. The horizontalaxis 461 represents the hours in a twenty-four (24) hour time period,while the vertical axis 463 represents a number of wireless clientdevices (wireless clients).

FIG. 4 also includes three (3) curves/lines referred to as curves 464,465, and 466. Curve 464 represents the total number of wireless clientdevices associated with a first access point (AP), identified as accesspoint “A1,” at different points in time during the illustrated 24 hourtime period. The total number of wireless client devices 464 associatedwith access point A1 at any given point is determined based on thenumber of wireless client devices connected to (joined with) the accesspoint A1. More specifically, when a wireless client device initiallyconnects to an access point, the wireless client device sends anassociation request. This association request carries useful informationthat provides good visibility into the wireless client device (e.g.,device type, vendor, operating system (OS), etc.). As such, using theseassociation requests, the wireless controller 222 can determine whichwireless client devices are connected to access point A1 at any giventime.

The total number of wireless client devices 464 associated with accesspoint A1 at any given point in time includes both “stationary” wirelessclient devices, represented by curve 465, and mobile wireless clientdevices, represented by curve 466. As used herein, the differencebetween a mobile wireless client device and a stationary wireless clientdevice is based on predefined time windows. In particular, if a wirelessclient device stays connected to the access point AP1 throughout theentire (i.e., the duration of) predefined time window, then the wirelessclient device is labeled as a stationary wireless client device.However, if a wireless client device does not stay connected to theaccess point AP1 throughout the entire predefined time window, then thewireless client device is labeled as mobile wireless client device.

As noted, an association request carries useful information thatprovides good visibility into the wireless client device (e.g., devicetype, vendor, operating system (OS), etc.). In general, this informationused for device classification to distinguish non-movable devices suchas wireless printers, scanners, tagging devices, workstations, etc. frommobile devices such as smartphones, laptops, tablets, etc. Theseparameters will help create a database to ultimately deduce mobileclients from the overall client count. In certain examples, thedetermination of whether a device is a mobile wireless client device ora stationary wireless client devices is based on the client associationduration. When a wireless client device is associated to the accesspoints, it keeps sending periodic “heartbeat” message to keep theconnection alive. When a wireless client device initiates roam, it sendsa “death” message to the previously connected access point and sendsanother association to request to the next access point.

Therefore, analyzing association trends with active session informationand RSSI variation, the wireless controller 222 (e.g., adaptive pathloss correction module 225) can isolate stationary wireless clientdevices from mobile wireless client devices in the wireless network 210.This information may be useful, for example, for a few purposes. First,this information may be used by the wireless controller 222 to determinewhen to refresh benchmarks when, for example, a localized sector has amajority of the clients identified as stationary. Second, thisinformation may be used to probe further into the mobile client devicesin the network and corresponding resulting variances seen in path loss.

As noted, an identification of a wireless client device as stationarydoes not necessarily equate to a fixed position through the day, butinstead is based on the predefined window. For example, wireless clientdevices (e.g., office printers, wireless projectors, sensors, etc.)present on an office floor would remain stationary indefinitely.However, other stationary wireless client devices (e.g., laptopcomputers, tablet computers, etc.) may not have a fixed position and/ormay only be present in the office space 237 during certain hours of theday. Therefore, as shown by curve 465, the number of stationary wirelessclient devices may fluctuate throughout the day.

In general, the predefined time window used to determine whether awireless client device is a mobile wireless client device or astationary wireless client device can be variable. For example, incertain examples, the predefined time window can be adjusted based onchanges in the density and mobility of individual RF environment.Different predefined time windows can be established throughout the day,where initial benchmarks can be refreshed

As noted above, variations in path loss between neighboring accesspoints is generally due to the fact that the bodies of these personspresent in a wireless network site will absorb some portion of theneighbor messages sent between neighboring access points. As such, sincethese variations in path loss are primarily driven by moving objects onthe floor, the techniques presented herein rely on the number of mobilewireless client devices 466 to determine the impact of the presence ofpersons (bodies) on path loss. As such, the number of stationarywireless client devices 465 can be subtracted from the total number ofwireless client devices 464 to determine the number of mobile wirelessclient devices 466 (i.e., curve 466 reflects the difference betweencurve 464 and 465, at each corresponding point in time).

In the example of FIG. 4, a trend of increasing mobile wireless clientdevice count can be seen as beginning around 6:00 AM in the morning,with a first peak occurring around 11:30 AM. A trough in mobile wirelessclient device count is observed around 12:00 PM, followed by a secondpeak around 4 PM. Therefore, the mobile wireless client device countdecreases and falls to zero (0) around 9:00 PM. The example of FIG. 4illustrates two aspects of the techniques presented herein. First, witha fixed window size (e.g., at 1800 sec), the example shows a gradualincrease in stationary wireless client devices throughout the day.Second, the specific example of FIG. 4 illustrates an opportunity toenable different benchmark refresh periods (predefined time windows foridentification of stationary clients) between 11:30 AM and 1 PM as thesystem load goes down significantly.

In general, the number of mobile wireless client devices 466 (mobilewireless client device count) can be used determine the mobile wirelessclient device density trend(s) for the office space 237, or a portionthereof. In practice, a wireless network, such as wireless network 210,may include multiple access points for which mobile wireless clientdevice counts can be obtained. These mobile wireless client devicecounts from the multiple different access points can be used by wirelesscontroller 222 to generate mobile client density variation trends forwireless network 210, or a segment of wireless network 210.

As noted above, adaptive path loss correction module 225 generates acorrelation bias factor based, in part, on one or more neighbor messagepower variation trends between at least first and second access pointswithin a wireless network location, such as office space 237 or asection/segment of office space 237. As such, in accordance withexamples presented herein, the adaptive path loss correction module 225(or another entity) is configured to determine neighbor message powervariation trends for neighboring access points within the office space237.

As used herein, the phrase “neighbor message power variation trend”refers to fluctuations or changes in the neighbor messages sent betweenneighboring access points within an area of a wireless network location(e.g., a section of office space 237) over a time period. In particular,referring specifically to FIG. 5 shown is a graph 568 having a first(horizontal) axis 561 and a second (vertical) axis 563. The horizontalaxis 561 represents the hours in a twenty-four (24) hour time period,while the vertical axis 563 represents a compensated neighbor signalpower in decibels (dB) with reference to one milliwatt (mW).

FIG. 5 also includes two (2) curves/lines referred to as curves 569 and570. Curve 569 represents a first compensated neighbor signal powerassociated with neighbor signals received at access point A1, above,from a first neighboring access point, referred to as access point “A3,”at different points in time during the same 24 hour time period of FIG.4. In addition, curve 570 represents a second neighbor compensatedsignal power associated with neighbor messages received at access pointA1, above, from a second neighboring access point, referred to as accesspoint “A2,” at different points in time during the illustrated 24 hourtime period. FIG. 5 illustrates the compensated neighbor signal powersfor neighbor messages received at access point A1 from each of accesspoints A2 and A3 at a first frequency (e.g., 2.4 GHz).

Referring next to FIG. 6 shown is a graph 668 having a first(horizontal) axis 661 and a second (vertical) axis 663. The horizontalaxis 661 represents the hours in a twenty-four (24) hour time period,while the vertical axis 663 represents a compensated neighbor signalpower in decibels (dB) with reference to one milliwatt (mW).

FIG. 6 also includes two (2) curves/lines referred to as curves 669 and670. Curve 669 represents a first compensated neighbor signal powerassociated with neighbor signals received at access point A1, above,from the first neighboring access point A2 at different points in timeduring the same 24 hour time period of FIG. 4. In addition, curve 670represents a second neighbor compensated signal power associated withneighbor messages received at access point A1, above, from the secondneighboring access point A3 at different points in time during theillustrated 24 hour time period. FIG. 6 illustrates the compensatedneighbor signal powers for neighbor messages received from each ofaccess points A2 and A3 at a second frequency (e.g., 5 GHz).

Neighboring access points may be of different types, may use differenttransmit powers, and/or may have other variations. Therefore, tofacilitate illustration, the power associated with the neighbor messageshave, in FIGS. 5 and 6, been normalized/compensated based on theoperational attributes of the transmitting and/or receiving accesspoint. For example, if it is known what power was used to transmit aneighbor message, then this information can be used as part of anormalization process to generate the values shown in FIGS. 5 and 6.Therefore, as used herein, the term “compensated neighbor signal power”refers to the power of neighbor messages sent between access points thathave been normalized/compensated to facilitate illustration.

Additionally, neighbor messages are susceptible to received signalvariance due to the fact that transmit parameters can vary betweenneighboring endpoints (e.g., by the allowed power limited betweendiverse regulatory domains and power budget of the access points). Inorder to minimize false positives, the wireless controller 222, namelythe adaptive path loss correction module 225, first learns the factorsthat can induce such variation and then applies methods to denoise thisdelta.

In operation, the wireless controller 222, namely the adaptive path losscorrection module 225, will add complementary bias on power (e.g., RSSI)of neighbor messages received from a neighboring access point tominimize RSSI variance due to differences in operational attributes,such as Transmit Data Rate (CCK/OFDM), difference in total conductedpower at which the neighbor message is transmitted, channel fadingaberration due to Frequency Domain (Primary Frequency, UNI-BandInformation, etc.) and Spectrum Identifier for a multi band radio'sreception capabilities. These factors are used to calculate thecompensated neighbor signal power on the neighbor messages. The adaptivepath loss correction module 225 determines trends in the powers ofreceived neighbor messages (e.g., based on the compensated neighborsignal powers at various endpoints), to generate the one or moreneighbor message power variation trends for a wireless network location.

It is to be appreciated that, merely for ease of illustration, FIGS. 5and 6 only illustrate the compensated neighbor signal powers forneighbor messages received from two neighboring access points. However,an access point may receive neighbor messages from a greater number ofaccess points. As such, in practice, compensated neighbor signal powersmay be determined for greater numbers of neighboring access points atany given access point. Additionally, compensated neighbor signal powersmay be computed for many different combinations of neighboring accesspoints within a wireless network (e.g., determine compensated neighborsignal powers at each of access points A1, A2, and A3 with respect toeach of the other access points).

Referring next to FIG. 7, shown is a graph 772 that includes three (3)curves. In particular, graph 772 includes curve 466 (FIG. 4)illustrating the number of mobile wireless client devices associatedwith access point A1, at each point in time during the 24 hour timeperiod of FIGS. 4, 5, and 6. Additionally, FIG. 7 includes curve 573 and674. Curve 573 is the variation, in dBM, between curves 569 and 570 ofFIG. 5, while curve 674 is the variation, again in dBM, between curves669 and 670 of FIG. 6. In other words, FIG. 7 illustrates the variationin compensated neighbor signal powers determined in FIGS. 5 and 6(represented by curves 573 and 674, respectively), superimposed on themobile wireless client device count 466 of FIG. 4.

FIG. 7 generally illustrates that, during the 24 hour time period, thereis a strong correlation between mobile client/station density (bodies onthe floor) and the variations in the power of the neighbor messages(AP-AP measurements). That is, as can be seen by curves 466, 573, and674, the variances/fluctuations in the power of the neighbor messagesgenerally correspond to times when access point A1 is relatively moreloaded, meaning times when there are more mobile wireless client devicesassociated with access point A1 and, as such, there are more persons(bodies) in the area of access point A1.

This correlation between mobile wireless client device density andneighbor message power variations is what is leveraged in the techniquespresented herein to determine/generate one or more correlation biasfactors for access point A1 and, potentially, additional access pointsin the wireless network. That is, in accordance with embodimentspresented herein, when the network is loaded (wireless client devicesconnected), the techniques presented herein determine the neighbormessage power variations likely to seen by neighboring access points dueto the presence of the bodies in the wireless network location. Once thevariations are determined, these variations are converted, using themobile wireless client device density, into a weighting or offset factorthat can be applied to determine a path loss between neighboring accesspoints. For example, in the example of FIGS. 4-7, a correlation biasfactor of approximately 4.5 dBm in 2.4 GHz and approximately 11 dBm for5 GHz for access point A1 may be generated. In operation, thiscorrelation bias is then feedback into the path loss determinationprocess (e.g., at path loss determination module 224) for calculation ofan adjusted, and more correct, path loss associated with A1.

Stated differently, in accordance with the techniques presented herein,as data is gathered from various access points and in different RFsectors (i.e., different regions of a wireless network), the techniquespresented herein identify correlations between mobile wireless clientdevice density per RF Sector, Time-of-Day (ToD), and resultingvariations seen in the power of neighbor messages (e.g., transmit andreceive neighbor's RSSI) in the RF sectors. These correlations are thenused to generate the one or more correlation bias factors for accesspoints in the different RF sectors. For RF sectors with highercorrelation bias factors, the techniques presented herein may furtherrefine the bias factors by de-noising the data set and then applyingrule based techniques such as an apriori algorithm, Association RuleLearning (ARL), etc. It is to be noted that, due to the use of NeighborDiscovery Smoothing algorithms on certain wireless local area network(WLAN) controllers, there is a gradual decrease in both 2.4 GHz and 5GHz received neighbor signals at certain times.

As noted, FIGS. 4-7 generally illustrate the determination of one ormore correlation bias factors for a first access point A1 in wirelessnetwork 210. In practice, one or more correlation bias factors may begenerated for multiple access points in wireless network 210.Additionally as described further below, the operations described withreference to FIG. 3 and FIGS. 4-7 can be performed for the entirewireless network 210, or can be performed separately for different RFregions/areas of wireless network 210 (i.e., RF sectors). That is, oneor more correlation bias factors can be independently generated for eachof a number of different RF regions of the wireless network 210 so thatthese different regions may apply different corrections dictated by theone or more mobile client density variation trends and the one or moreneighbor message power variation trends for that RF region. As such, asused herein, the term “wireless network location” can refer to thephysical areas associated with an entire wireless network or thephysical area associated within only a specific RF segment of a wirelessnetwork.

FIG. 8 is a schematic flow diagram illustrating aspects of thetechniques presented herein. First, FIG. 8 illustrates a plurality ofaccess points 820 that are positioned within a wireless network locationand collectively form at least part of a wireless network. Second, FIG.8 illustrates a plurality of wireless client devices (wireless clients)828 that may be positioned within the wireless network location and thatgenerally connect to the wireless network formed by access points 820.The wireless client devices 828 may include devices that have a fixedposition (e.g., printers, desktop computers, etc.) or devices (e.g.,mobile phones, laptop computers, etc.) that can be physically movedwithin, or removed from, the wireless network location.

The flow of FIG. 8 begins at 875 where a wireless controller or othercomputing device (not shown in FIG. 8) organizes the access points 820into localized RF sectors (i.e., groups of access points co-locatedwithin a physical area). FIG. 8 illustrates an example in which theaccess points 820 are organized into three different RF sectors,referred to as RF sectors 876(1), 876(2), and 876(3).

Next, at 877, the wireless controller uses the information regarding theRF sectors to determine a wireless client device density, per RF sector876(1), 876(2), and 876(3) (e.g., as described above with reference toFIG. 4). At 878, the wireless client device density per RF sector876(1), 876(2), and 876(3), along with a dynamically adjustable sessionwindow 879, is used to identify stationary wireless client devices(e.g., as described above with reference to FIG. 4). The result is thedetermination of the mobile wireless client device density trends 880,per RF sector 876(1), 876(2), and 876(3) (i.e., determine how the mobilewireless client device density changes/fluctuates over time).

Also shown in FIG. 8 is the establishment of a baseline for each RFsector 876(1), 876(2), and 876(3) at 881. In general, the baseline is aneighbor message (inter-Access Point neighbor signal) strength when thenetwork is minimally loaded (e.g., low or no wireless clientsconnected). At 882, the powers of the neighbor messages sent betweenaccess points in each of the RF sectors 876(1), 876(2), and 876(3) areobtained at one or more frequencies. The neighbor message powers areused to determine neighbor message power signal variation trends 883,per RF sector 876(1), 876(2), and 876(3) (e.g., as described above withreference to FIGS. 5 and 6).

As noted above, the mobile wireless client device density trends 880 andthe neighbor message power signal variation trends 883 are time seriesdata sets representing fluctuations in client density and neighbormessage power variations, respectively. At 884, the mobile wirelessclient device density trends 880 and the neighbor message power signalvariation trends 883 are used to determine correlation bias factors forthe access points 820.

In general the determined correlation bias factors may be different fordifferent access points and/or for different RF sectors. For example,FIGS. 9A, 9B, and 9C are schematic diagrams illustrating varyingcorrelation bias factors (correlation coefficients) for the different RFsectors 876(1), 876(2), and 876(3), each having different diversewireless mobile wireless client device densities.

Shown in FIGS. 9A, 9B, and 9C are mobile wireless client device densitytrends 880(1), 880(2), and 880(3) determined for the RF sectors 876(1),876(2), and 876(3), respectively. Also shown in FIGS. 9A, 9B, and 9C areneighbor message power signal variation trends 883(1), 883(2), and883(3) determined for the RF sectors 876(1), 876(2), and 876(3),respectively. FIGS. 9A, 9B, and 9C also include graphs 885(1), 885(2),and 885(3), respectively illustrating the determined correlation biasfactors determined for the RF sectors 876(1), 876(2), and 876(3),respectively. In FIGS. 9A-9C, the different patterned dots representRSSI bucketization of wireless client devices from an access point(e.g., >=−50 dBm; =−51 to −67 dBm; =<−67 dBm). In general, a slight biasvariation can be applied based on the client's count and distance fromthe access point.

As noted above, the techniques presented herein correlate receivedneighbor message power variation trends (e.g., received RSSI variationin the Neighbor Discovery frames) with mobile client density trends overa localized RF sector and time of the day. Additionally, in additionalaspects, localized RF sectors with high correlation coefficients canadjust their power control thresholds to compensate for received RSSIdegradations induced by the mobile client density.

In accordance with embodiments presented herein, the correlation biasfactors determined for an access point, RF sector, or wireless networkare used to determine path losses between neighboring access points.That is, in accordance with embodiments herein, a path loss betweenneighboring access points within a wireless network location can bedetermined using at least one correlation bias factor and dataassociated with neighbor messages sent between the neighboring accesspoints. For example, a path loss between two neighboring access pointsin a wireless network location is determined using inter-AP radiofrequency (RF) measurements (e.g., inter-AP Received Signal StrengthIndication (RSSI) values) obtained from the real-time neighbor messagessent between the two neighboring access points. The at least onecorrelation bias factor may then be applied to the determined path lossto correct for the presence of persons (bodies) within the wirelessnetwork location. The result is a corrected/compensated path loss (e.g.,a path loss that accounts for the presence of persons (bodies) withinthe wireless network location).

As noted above, the mobile client density within a wireless networklocation, and thus the number of moving bodies within the wirelessnetwork location, can vary throughout a time period. As such, thecorrelation bias factors in accordance with embodiments presented hereinmay be selectively applied, for example, only during certain timeperiods determined to correspond to time periods of sufficiently highmobile client density (e.g., time periods in which the mobile clientdensity exceeds a determined threshold). In certain examples, these timeperiods may be predetermined time periods (e.g., set time windows).However, in other examples, these time periods may be dynamicallydetermined based on a monitoring of the mobile client density. Forexample, in such embodiments, the mobile client density is monitored inreal-time. Once the mobile client density exceeds a determinedthreshold, the correlation bias factors are instantiated for use indetermination of path losses in the wireless network location.

In certain examples, the correlation bias factors determined for anaccess point, RF sector, or wireless network are static values that canbe selectively applied in determination of a path loss. For example, acorrelation bias factor of X dBm in 2.4 GHz and approximately Y dBm for5 GHz could be determined for an RF sector and only applied duringcertain time periods (e.g., the time periods that correspond tosufficiently high mobile client density). In other examples, thecorrelation bias factors determined for an access point, RF sector, orwireless network are dynamic/variable values that can be determined oradjusted in real-time based on the real-time mobile client densityvariation trends and/or neighbor message power variation trendsdetermined for the wireless network location.

An adjusted path loss determined in accordance with embodimentspresented herein may be used in a number of different manners, such asfor Radio Resource Management (RRM). For example, an adjusted path losscould be used to set/configure (e.g., adjust) the transmit power ofaccess points within a wireless network location (e.g., for transmitpower control (TPC)). In further examples, an adjusted path loss inaccordance with embodiments presented herein could be used to configuredynamic channel assignment (DCA) for access points in a wireless networklocation. In general, DCA considers neighbor's signal strength in orderto minimize frequency overlap between channels. The use of the adjustedpath loss for DCA may void overestimation of neighbor message signalsinitially set while the wireless network location is substantiallyempty.

In certain examples, an adjusted path loss in accordance withembodiments presented herein could be used for Flexible Radio Assignment(FRA). In conventional arrangements, variation in received neighbormessage power influences FRA to create more coverage holes when mobileclient density is at a peak. Therefore, using the techniques presentedherein, adjustments in received neighbor message signal can becompensated in the Coverage Overlap Factor (COF) computation. This willensure that the COF calculations are done with correct path lossdetermined, as described above, rather than readings initially set whilethe wireless network location is substantially empty.

In other examples, an adjusted path loss in accordance with embodimentspresented herein could be used for Coverage Hole Detection andMitigation (CDM). Similar to inter-AP NDP exchanges, a non-LOS signalbetween a first wireless client device and the serving access point cansignificantly deteriorate based on the density of other wireless clientdevices around the first wireless client device. The techniquespresented herein to compute the level of signal deterioration between anaccess point and the first wireless client device and compute estimatedDL RSSI at the station, which then can be feedback to the CHDM algorithmso that the RRM reacts based on the “true” downlink quality (and notjust on the access point view).

It is to be appreciated that the above uses of correlation bias factorsand/or adjusted path losses are illustrative and that these values canbe used in a number of different manners for control of a wirelessnetwork. In certain embodiments, conducted power for OTA transmissionscan be artificially stamped to reflect increase in the additional pathloss. For example, neighbor messages sent at the highest power in 2.4GHz (e.g., at 23 dBm) can stamp conducted power as 17 dBm in highcorrelation bias factor sites with observed increase in path lossexceeding 6 dBm. As used herein, stamping generally refers to embeddingnew transmit power (e.g., NDP TxPower) based on the observed variation.For example, localized networks that observe consistent 6 dBm ofdegradation due to higher mobile density throughout the day, can havetransmit power of −6 dB stamped as the artificial power stamped for theneighbor messages (NDP frames).

Moreover, path loss variations conducted at the RF sectors can providefeedback into the correlation estimates and models can predict how muchadditional path adjustment needs to be made at varying times of the daybased on the previously observed client density and association trends.Basically, as the system is calculating path loss variation based on themobile client device density across different RF sectors at a customersite, learnings from one site can be used in estimation of path lossdegradation to another site with similar RF density and client deviceload.

FIG. 10 is a block diagram of a wireless controller 1022 configured toperform the techniques presented herein. The wireless controller 1022includes at least one processor 1090, memory 1091, a bus 1092 and anetwork interface unit 1093. The at least one processor 1090 may be amicroprocessor or microcontroller. The network interface unit 1093facilitates network communications between the wireless controller 1022and network nodes (e.g., access points). The processor 1090 executesinstructions associated with software stored in memory 1091.Specifically, the memory 1091 path loss determination logic 1024 that,when executed by the processor 1090, performs the operations of the pathloss determination modules 124, 224 of FIGS. 1 and 2, respectively. Thememory 1091 also stores adaptive path loss correction logic 1025 that,when executed by the processor 1090, performs the operations of theadaptive path loss correction modules 125, 225 of FIGS. 1 and 2,respectively. Finally, the memory 1091 stores Radio Resource Managementlogic 1026 that, when executed by the processor 1090, performs the RadioResource Management operations of the Radio Resource Management modules126, 226 of FIGS. 1 and 2, respectively (e.g., dynamic channelassignment operations, transmit power control operations, etc.).

The memory 1091 may comprise read only memory (ROM), random accessmemory (RAM), magnetic disk storage media devices, optical storage mediadevices, flash memory devices, electrical, optical, or otherphysical/tangible memory storage devices. In general, the memory 1091may comprise one or more tangible (non-transitory) computer readablestorage media (e.g., a memory device) encoded with software comprisingcomputer executable instructions and when the software is executed (bythe processor 1090) it is operable to perform the operations describedherein.

It is to be appreciated that the specific arrangement of wirelesscontroller 1022 shown in FIG. 10 is illustrative. It is also to beappreciated that the functions of the wireless controller 1022 may bevirtualized, and for example, performed by an application running in adata center/cloud computing environment.

As noted above, presented herein are techniques for correlating mobilewireless client device density trends with neighbor message powervariations (inter-access point received signal power variations) in awireless network to determine correlation bias factors. The correlationbias factors are used to determine accurate/adjusted path losses betweenaccess points that compensate for the presence of persons (bodies)within the wireless network location and/or communicated between accesspoints as part of the neighbor messages. As a result, operations of theaccess points can be controlled in an optimal manner.

In one aspect, a method is provided. The method comprises: determiningone or more mobile client density variation trends in a wireless networklocation; determining one or more neighbor message power variationtrends between at least first and second access points within thewireless network location; generating at least one correlation biasfactor using the one or more mobile client density variation trends andthe one or more neighbor message power variation trends; and determininga path loss between at least the first and second access points based onthe correlation bias factor and data associated with neighbor messagessent between the first and second access points.

In another aspect, an apparatus is provided. The apparatus comprises:one or more network interface units; a memory; and at least oneprocessor configured to: determine one or more mobile client densityvariation trends in a wireless network location; determine one or moreneighbor message power variation trends between at least first andsecond access points within the wireless network location; generate atleast one correlation bias factor using the one or more mobile clientdensity variation trends and the one or more neighbor message powervariation trends; and determine a path loss between at least the firstand second access points based on the correlation bias factor and dataassociated with neighbor messages sent between the first and secondaccess points.

In another aspect, one or more non-transitory computer readable storagemedia are provided. The non-transitory computer readable storage mediaare encoded with instructions that, when executed by a processor, causethe processor to: determine one or more mobile client density variationtrends in a wireless network location; determine one or more neighbormessage power variation trends between at least first and second accesspoints within the wireless network location; generate at least onecorrelation bias factor using the one or more mobile client densityvariation trends and the one or more neighbor message power variationtrends; and determine a path loss between at least the first and secondaccess points based on the correlation bias factor and data associatedwith neighbor messages sent between the first and second access points.

The above description is intended by way of example only. Variousmodifications and structural changes may be made therein withoutdeparting from the scope of the concepts described herein and within thescope and range of equivalents of the claims.

What is claimed is:
 1. A method comprising: determining one or moremobile client density variation trends in a wireless network location;determining one or more neighbor message power variation trends betweenat least first and second access points within the wireless networklocation; generating at least one correlation bias factor by analyzingthe one or more mobile client density variation trends and the one ormore neighbor message power variation trends with respect to time-of-dayinformation to determine periods of time having fluctuations in neighbormessage power and a predetermined mobile client density; and determininga path loss between at least the first and second access points based onthe correlation bias factor and data associated with neighbor messagessent between the first and second access points.
 2. The method of claim1, further comprising: using the determined path loss to perform one ormore Radio Resource Management operations for one or more of the firstor second access points.
 3. The method of claim 2, wherein using thedetermined path loss to perform one or more Radio Resource Managementoperations for one or more of the first or second access pointscomprises: configuring a transmit power of one or more of the first orsecond access points.
 4. The method of claim 2, wherein using thedetermined path loss to perform one or more Radio Resource Managementoperations for one or more of the first or second access pointscomprises: performing one or more of dynamic channel assignment (DCA),Flexible Radio Assignment (FRA), or Coverage Hole Detection andMitigation (CDM) for one or more of the first or second access points.5. The method of claim 1, further comprising: stamping neighbor messagessent to one or more of the first and second access points with one ormore of the at least one correlation bias factor or a modified transmitpower selected based on the correlation bias factor.
 6. The method ofclaim 1, wherein determining neighbor message power variation trendsbetween at least the first and second access points includes:monitoring, over a time period, receive signal strength informationassociated with neighbor messages sent to one or more of the first andsecond access points from neighboring access points in the wirelessnetwork location.
 7. The method of claim 1, wherein determining one ormore mobile client density variation trends in a wireless networklocation includes: monitoring, over a time period, a total number ofwireless client devices connected to each of the first and second accesspoints at different points in the time period; monitoring, over the timeperiod, a number of stationary wireless client devices connected to eachof the first and second access points at different points in the timeperiod; and using the total number of wireless client devices and thenumber of stationary wireless client devices connected to each of thefirst and second access points to identify a number of mobile clientdevices connected to each of the first and second access points atdifferent points in the time period.
 8. The method of claim 7, whereinmonitoring, over the time period, the number of stationary wirelessclient devices connected to each of the first and second access pointsat different points in the time period comprises: determining which ofthe total number of wireless client devices have been connected to oneor more of the first or second access points for a duration of apredefined time window.
 9. An apparatus, comprising: one or more networkinterface units; a memory; and at least one processor communicativelycoupled to the one or more network interface units and the memory, theat least one processor configured to: determine one or more mobileclient density variation trends in a wireless network location;determine one or more neighbor message power variation trends between atleast first and second access points within the wireless networklocation; generate at least one correlation bias factor by analyzing theone or more mobile client density variation trends and the one or moreneighbor message power variation trends with respect to time-of-dayinformation to determine periods of time having fluctuations in neighbormessage power and a predetermined mobile client density; and determine apath loss between at least the first and second access points based onthe correlation bias factor and data associated with neighbor messagessent between the first and second access points.
 10. The apparatus ofclaim 9, wherein the at least one processor is further configured to:use the determined path loss to perform one or more Radio ResourceManagement operations for one or more of the first or second accesspoints.
 11. The apparatus of claim 10, wherein to use the determinedpath loss to perform one or more Radio Resource Management operationsfor one or more of the first or second access points, the at least oneprocessor is configured to: configure a transmit power of one or more ofthe first or second access points.
 12. The apparatus of claim 10,wherein to use the determined path loss to perform one or more RadioResource Management operations for one or more of the first or secondaccess points, the at least one processor is configured to: perform oneor more of dynamic channel assignment (DCA), Flexible Radio Assignment(FRA), or Coverage Hole Detection and Mitigation (CDM) for one or moreof the first or second access points.
 13. The apparatus of claim 9,wherein the at least one processor is configured to: stamp neighbormessages sent to one or more of the first and second access points withone or more of the at least one correlation bias factor or a modifiedtransmit power selected based on the correlation bias factor.
 14. Theapparatus of claim 9, wherein to determine neighbor message powervariation trends between at least the first and second access points,the at least one processor is configured to: monitor, over a timeperiod, receive signal strength information associated with neighbormessages sent to one or more of the first and second access points fromneighboring access points in the wireless network location.
 15. Theapparatus of claim 9, wherein to determine one or more mobile clientdensity variation trends in a wireless network location, the at leastone processor is configured to: monitor, over a time period, a totalnumber of wireless client devices connected to each of the first andsecond access points at different points in the time period; monitor,over the time period, a number of stationary wireless client devicesconnected to each of the first and second access points at differentpoints in the time period; and use the total number of wireless clientdevices and the number of stationary wireless client devices connectedto each of the first and second access points to identify a number ofmobile client devices connected to each of the first and second accesspoints at different points in the time period.
 16. The apparatus ofclaim 15, wherein to monitor, over the time period, the number ofstationary wireless client devices connected to each of the first andsecond access points at different points in the time period, the atleast one processor is configured to: determine which of the totalnumber of wireless client devices have been connected to one or more ofthe first or second access points for a duration of a predefined timewindow.
 17. One or more non-transitory computer readable storage mediaencoded with instructions that, when executed by a processor, cause theprocessor to: determine one or more mobile client density variationtrends in a wireless network location; determine one or more neighbormessage power variation trends between at least first and second accesspoints within the wireless network location; generate at least onecorrelation bias factor by analyzing the one or more mobile clientdensity variation trends and the one or more neighbor message powervariation trends with respect to time-of-day information to determineperiods of time having fluctuations in neighbor message power and apredetermined mobile client density; and determine a path loss betweenat least the first and second access points based on the correlationbias factor and data associated with neighbor messages sent between thefirst and second access points.
 18. The non-transitory computer readablestorage media of claim 17, further comprising instructions operable to:use the determined path loss to perform one or more Radio ResourceManagement operations for one or more of the first or second accesspoints.
 19. The non-transitory computer readable storage media of claim17, further comprising instructions operable to: use the determined pathloss to perform one or more Radio Resource Management operations for oneor more of the first or second access points.
 20. The non-transitorycomputer readable storage media of claim 17, further comprisinginstructions operable to: stamp neighbor messages sent to one or more ofthe first and second access points with one or more of the at least onecorrelation bias factor or a modified transmit power selected based onthe correlation bias factor.